Australian Sphingidae – DNA Barcodes Challenge Current Species Boundaries and Distributions
Bibliographic record
Abstract
MAIN OBJECTIVE: We examine the extent of taxonomic and biogeographical uncertainty in a well-studied group of Australian Lepidoptera, the hawkmoths (Sphingidae). METHODS: We analysed the diversity of Australian sphingids through the comparative analysis of their DNA barcodes, supplemented by morphological re-examinations and sequence information from a nuclear marker in selected cases. The results from the analysis of Australian sphingids were placed in a broader context by including conspecifics and closely related taxa from outside Australia to test taxonomic boundaries. RESULTS: Our results led to the discovery of six new species in Australia, one case of erroneously synonymized species, and three cases of synonymy. As a result, we establish the occurrence of 75 species of hawkmoths on the continent. The analysis of records from outside Australia also challenges the validity of current taxonomic boundaries in as many as 18 species, including Agrius convolvuli (Linnaeus, 1758), a common species that has gained adoption as a model system. Our work has revealed a higher level of endemism than previously recognized. Most (90%) Australian sphingids are endemic to the continent (45%) or to Australia, the Pacific Islands and the Papuan and Wallacean regions (45%). Only seven species (10%) have ranges that extend beyond this major biogeographical boundary toward SE Asia and other regions of the Old World. MAIN CONCLUSIONS: This study has established that overlooked cryptic diversity and inaccurate species delineation produced significant misconceptions concerning diversity and distribution patterns in a group of insects that is considered well known taxonomically. Because DNA barcoding represents a straightforward way to test taxonomic boundaries, its implementation can improve the accuracy of primary diversity data in biogeography and conservation studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".